QUIRMIA-A Phenotype-Based Algorithm for the Inference of Quinolone Resistance Mechanisms in <i>Escherichia coli</i>

• Main Authors: Frank Imkamp (Author), Elias Bodendoerfer (Author), Stefano Mancini (Author)
• Format: Book
• Published: MDPI AG, 2023-06-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.3390/antibiotics12071119; 2079-6382
• Keywords: quinolone resistance; <i>E. coli</i>; whole genome sequencing; diagnostic algorithm; phenotype/genotype correlation; Therapeutics. Pharmacology; RM1-950
• Source: Antibiotics, Vol 12, Iss 7, p 1119 (2023)

Objectives: Quinolone resistance in <i>Escherichia coli</i> occurs mainly as a result of mutations in the quinolone-resistance-determining regions of <i>gyrA</i> and <i>parC</i>, which encode the drugs' primary targets. Mutational alterations affecting drug permeability or efflux as well as plasmid-based resistance mechanisms can also contribute to resistance, albeit to a lesser extent. Simplifying and generalizing complex evolutionary trajectories, low-level resistance towards fluoroquinolones arises from a single mutation in <i>gyrA</i>, while clinical high-level resistance is associated with two mutations in <i>gyrA</i> plus one mutation in <i>parC</i>. Both low- and high-level resistance can be detected phenotypically using nalidixic acid and fluoroquinolones such as ciprofloxacin, respectively. The aim of this study was to develop a decision tree based on disc diffusion data and to define epidemiological cut-offs to infer resistance mechanisms and to predict clinical resistance in <i>E. coli</i>. This diagnostic algorithm should provide a coherent genotype/phenotype classification, which separates the wildtype from any non-wildtype and further differentiates within the non-wildtype. Methods: Phenotypic susceptibility of 553 clinical <i>E. coli</i> isolates towards nalidixic acid, ciprofloxacin, norfloxacin and levofloxacin was determined by disc diffusion, and the genomes were sequenced. Based on epidemiological cut-offs, we developed a QUInolone Resistance Mechanisms Inference Algorithm (QUIRMIA) to infer the underlying resistance mechanisms responsible for the corresponding phenotypes, resulting in the categorization as "susceptible" (wildtype), "low-level resistance" (non-wildtype) and "high-level resistance" (non-wildtype). The congruence of phenotypes and whole genome sequencing (WGS)-derived genotypes was then assigned using QUIRMIA- and EUCAST-based AST interpretation. Results: QUIRMIA-based inference of resistance mechanisms and sequencing data were highly congruent (542/553, 98%). In contrast, EUCAST-based classification with its binary classification into "susceptible" and "resistant" isolates failed to recognize and properly categorize low-level resistant isolates. Conclusions: QUIRMIA provides a coherent genotype/phenotype categorization and may be integrated in the EUCAST expert rule set, thereby enabling reliable detection of low-level resistant isolates, which may help to better predict outcome and to prevent the emergence of clinical resistance.